Systems and methods for premises monitoring

ABSTRACT

Monitoring a premises may comprise receiving signals from a plurality of devices. One or more spatially static devices of the plurality of devices may be determined based on strengths of the received signals. A user activity pattern may be determined based on changes in strengths of signals received from the spatially static devices. Abnormal user activity may be determined based on the user activity pattern and a strength of at least one signal received from at least one of the spatially static devices.

BACKGROUND

Presence detection or in-home behavior monitoring systems may be used todetect an intruder or identify normal and abnormal user behavior in ahome. Existing presence detection or in-home behavior monitoring systemssuffer from several drawbacks. For example, the systems often intrude onuser privacy. As another example, the systems may cause userinconvenience by requiring a user to routinely and actively engage withcomponents of the system. Improvements in presence detection or in-homebehavior monitoring systems are needed.

SUMMARY

Devices, such as user devices, entertainment devices, and/or homeautomation devices (for example, Internet of Things (IOT) devices), maybe located at a premises. Based on strengths of signals output by thedevices over time, such as based on signal strength fluctuations, it maybe determined that one or more of the devices comprise a spatiallystatic device. The spatially static device may comprise a device that isnot frequently moved by users.

The strength of signals output by the spatially static device may beaffected by the presence and/or movement of a user, and such effect onthe output signals can be measured by receiving devices. For example,the body of the user may absorb and/or interfere with the signals.Therefore, a user activity pattern may be determined based on strengthsof the signals over time (e.g., times of day), such as based on signalstrength fluctuations over time. Patterns of the strengths of thesignals may be determined. The patterns of signal strength may beassociated with normal user behavior, such as associated with absorptionof interference of the signals occurring when a user follows a normaldaily routine. Patterns of strengths of received signals may be comparedto the patterns determined to be associated with normal user behavior inorder to determine that captured signals represent normal or abnormalbehavior.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings show generally, by way of example, but not by wayof limitation, various examples discussed in the present disclosure. Inthe drawings:

FIG. 1 shows an example operating environment.

FIG. 2 shows an example operating environment.

FIG. 3 shows an example method.

FIG. 4 shows example pattern data.

FIG. 5 shows an example pattern.

FIG. 6 shows an example method.

FIG. 7 shows an example method.

FIG. 8 shows an example method.

FIG. 9 shows an example computing device.

DETAILED DESCRIPTION

One or more spatially static devices may be located at a premises. Thespatially static devices may comprise user devices, such as desktopcomputers or televisions, as examples. The spatially static devices maycomprise Internet of Things (IoT) devices. The spatially static devicesmay comprise premises management devices. The premises managementdevices may comprise security system devices, such as sensors, alarms,cameras, and/or control panels. The premises management devices maycomprise home automation devices, lighting devices, thermostats, garagedoor openers, locks, digital assistants, and/or smart appliances. Thespatially static devices may comprise entertainment devices, such asset-top boxes, audio devices, gaming systems, and/or media players.

Spatially static devices may comprise devices that are not frequentlymoved by users; their position in a space may be stationary, and theymay remain substantially static over time. For example, a spatiallystatic device may comprise a device that is not portable, such as alarge device and/or a heavy device. For example, a user may not move alarge television or a large speaker on a daily or weekly basis. Thespatially static device may comprise a device that is fixed, secured,and/or installed at a premise. For example, a user may not move premisesmanagement system devices, such as a thermostat device, a motion sensor,an alarm, and/or a lighting device mounted on a wall of a house. Thespatially static device may comprise a device that is configured to beconnected to one or more peripheries or external devices, such as anexternal power source or an output device. Cords and other peripheriesconnected to the device may make it inconvenient for a user to move thedevice. For example, a home computer that is connected to a monitor, akeyboard, a mouse, and/or a power source may not be moved by a user on adaily or weekly basis. The spatially static device may comprise a devicethat is not frequently moved by a user during normal use of the device,such as a lighting device and/or a premises management device (e.g., ahome automation device and/or a security system device).

A gateway device may be located at the premises and may configured tocommunicate with the spatially static devices. Signals received by thegateway device from the spatially static devices may have associatedreceived signal strength indicators (RSSIs). The RSSI may comprise ameasurement of the power level of the signal. The RSSI associated with asignal received from a spatially static device may be determined by thegateway device.

Movement and/or presence of user at the premises may cause absorptionand/or interference of the signals from the spatially static devices.The absorption and/or interference of the signals may affect the RSSIsof the signals, such as by causing the RSSIs to fluctuate. For example,the RSSI associated with a signal from a spatially static device may beweaker as a result of a user being near the spatially static devicecompared to an RSSI associated with a signal when no one is around thespatially static device. Machine learning models may be used tounderstand how the spatially static devices are arranged in a premiseswithout a user's input regarding such arrangement of spatially staticdevices at a premises. The machine learning models may use RSSI dataassociated with the spatially static devices and/or patterns of the RSSIvalues.

RSSIs of signals output by the spatially static devices may be used todetermine the spatially static devices. For example, the spatiallystatic devices may be identified from a group of devices that mayinclude non-spatially static devices, such as wearable devices and/ormobile devices, as examples. Since spatially static devices are notconstantly moved, RSSI values associated with signals received from thespatially static devices may comprise distinct features compared to RSSIvalues of non-spatially static devices. For example, an RSSI associatedwith a signal from a spatially static device may exhibit fewerfluctuations than an RSSI associated with a signal received from anon-spatially static device.

RSSIs of signals output by the spatially static devices may be used todetermine clusters of spatially static devices. The clusters of devicesmay comprise groups of devices that are spatially-related, such aswithin a particular room of area. RSSIs associated with signals outputby a cluster of spatially static devices may exhibit disruptions at thesame time or at similar times. Based on the RSSIs exhibitingdisruptions, the group of spatially static devices may be determined tobe in a same area and clustered together. For example, an RSSIassociated with signals output by a home assistant device in a kitchenand an RSSI associated with signals output by a smart refrigerator inthe kitchen may experience a disruption caused by a user walking throughthe kitchen. The home assistant device and the smart refrigerator may bedetermined to comprise a cluster, such as a cluster associated with thekitchen.

RSSIs patterns associated with normal user behavior may be determined.The patterns may be determined based on RSSI fluctuations acrossclustered areas. As an example, a user may arrive at a premises mostweekdays at 5:00 p.m. The user may enter the premises through thekitchen and generally spend about five minutes in the kitchen making asnack. The user may next enter the living room. As the user movesthrough the kitchen and living room, that movement may causefluctuations in the RSSI of signals received from the spatially staticdevices in those locations. An RSSI pattern associated with normalbehavior for the user may be determined based on the determinedfluctuations in the RSSIs of received signals.

Based on the RSSI pattern, a normal behavior profile for the user may begenerated. The normal behavior profile for the user may indicate thatthe user is expected to arrive at an area associated with the kitchen ator around 5:00 p.m. on weekdays. The normal behavior profile for theuser may indicate that the user is expected to stay in the areaassociated with the kitchen for about five minutes. The normal behaviorprofile for the user may indicate that the user is expected to arrive atan area associated with the living room after leaving the kitchen. Thenormal behavior profile may be determined by the gateway device and/or acomputing device located external to the premises. The gateway devicemay send an indication of the normal behavior profile and/or the RSSIpattern associated with normal behavior of the user to a computingdevice located external to the premises.

An intruder to the premises may break into the premises at 2:00 p.m. ona weekday. The intruder may enter the premises via a window in a livingroom. The intruder's movement and/or presence in the living room maycause RSSIs associated with signals output by spatially static devicesin the living room to fluctuate. An RSSI pattern may indicate normalRSSI of signals of the living room devices at 2:00 p.m. on a weekday.The RSSI values when the intruder enters the living room may at 2:00p.m. may differ from the RSSI values of the pattern associated withnormal behavior of a user of the premises. Based on the differencebetween the RSSI values of the signals received and the RSSI valuesindicated by the pattern, abnormal user behavior may be determined. Theabnormal user behavior may be determined by the gateway device and/or acomputing device located external to the premises. The gateway devicemay send an indication of the abnormal user behavior to a computingdevice located external to the premises.

Based on determining abnormal user behavior, an alert may be generated.The alert may be sent to a device associated with the premises. Thealert may be sent to a device associated with a user associated with thepremises. The alert may be sent to one or more emergency contacts, suchas a 911 dispatch, a hospital, a police office, and/or a fire station.The gateway device may generate the alert or cause the alert to begenerated. The gateway device may send the alert to a computing devicelocated external to the premises.

Determining abnormal user behavior may aid in care for the elderly.Elderly users may tend to have regular routines. Based on determiningabnormal user behavior in an elderly user, an alarm or alert may begenerated and sent to one or more relatives of the elderly user, anelderly care center management, a medical services provider, and/or anemergency services provider, as examples.

FIG. 1 shows an example operating environment. The operating environmentmay comprise a premises 100. The premises 100 may comprise a residentialstructure or unit, such as a house or an apartment unit. The premises100 may comprise a commercial structure or unit, such as a retail store.The premises 100 may comprise any premises wherein activity of users mayform a behavioral pattern and wherein monitoring of user behavior isdesired. The premises 100 may comprise one or more areas, such as afirst area 110, a second area 120, and a third area 130.

A gateway device 140 may be located at the premises 100. The gatewaydevice 140 may comprise a home automation device. The gateway device 140may be configured to control and communicate with a plurality ofdevices, such as devices located at a premises and devices external tothe premises. One or more spatially static devices may be located at thepremises 100. The spatially static devices may comprise a firstspatially static device 112, a second spatially static device 114, athird spatially static device 116, a fourth spatially static device 122,a fifth spatially static device 124, a sixth spatially static device126, a seventh spatially static device 132, an eighth spatially staticdevice 134, and a ninth spatially static device 136. The spatiallystatic devices may be configured to communicate with the gateway device140. For example, the spatially static devices may be configured tocommunicate with the gateway device 140 via Wi-Fi communication. A Wi-Fisignal or other communication signal output by one or more of thespatially static devices may have a received signal strength indicator(RSSI).

One or more of the spatially static devices may be located in an area ofthe premises 100. For example, the first spatially static device 112,the second spatially static device 114, and the third spatially staticdevice 116 may be located at a first area 110 of the premises 100. Thefirst area 110 may be associated with a first room of the premises 100.The fourth spatially static device 122, the fifth spatially staticdevice 124, and the sixth spatially static device 126 may be located ata second area 120 of the premises 100. The second area 120 may beassociated with a second room of the premises 100. The seventh spatiallystatic device 132, the eighth spatially static device 134, and the ninthspatially static device 136 may be located at a third area 130 of thepremises 100. The third area 130 may be associated with a third room ofthe premises 100.

The gateway device 140 may be configured to receive signals from thedevices located at the premises. The gateway device 140 may beconfigured to determine RSSI values associated with the signals. Thegateway device 140 may be configured to send an indication of the RSSIvalues, such as to a computing device 160 located external to thepremises 100. The gateway device 140 may be configured to send theindication of the RSSI values via a network 150. The network 150 maycomprise a private network. The network 150 may comprise a publicnetwork, such as the Internet. The network 150 may use a communicationprotocol, such as an Internet Protocol (IP).

The gateway device 140 and/or the computing device 160 may be configuredto classify the devices as spatially static devices or as non-spatiallystatic devices. For example, the devices may be classified based onRSSIs associated with signals output by the devices over a period oftime (e.g., a week, ten days, as examples), such as a training period.The gateway device 140 and/or the computing device 160 may be configuredto determine a magnitude of fluctuations of the RSSI values associatedwith the devices. The gateway device 140 and/or the computing device 160may be configured to classify the devices based on the determinedmagnitude of fluctuations of the RSSI values. The gateway device 140and/or the computing device 160 may be configured to classify devicesthat output signals having RSSIs having low magnitude fluctuations asspatially static devices. The gateway device 140 and/or the computingdevice 160 may be configured to classify devices that output signalshaving RSSIs having high magnitude fluctuations as non-spatially staticdevices. For example, the gateway device 140 and/or the computing device160 may classify the first spatially static device 112, the secondspatially static device 114, the third spatially static device 116, thefourth spatially static device 122, the fifth spatially static device124, the sixth spatially static device 126, the seventh spatially staticdevice 132, the eighth spatially static device 134, and the ninthspatially static device 136 as spatially static devices based on lowmagnitude fluctuations of RSSIs of signals output by the spatiallystatic devices.

The gateway device 140 and/or the computing device 160 may be configuredto classify the devices as spatially static devices or non-spatiallystatic devices based on connectivity of the devices. Connectivity of thedevices may be determined based on a percent of a period of time (e.g.,an hour, a day, a week, etc.) or a frequency at which the devices areconnected to (e.g., in communication with) another device at thepremises, such as the gateway device 140). The percent of time orfrequency at which the devices are connected to another device may bedetermined based on reception of signals from the devices by the otherdevice.

The gateway device 140 and/or the computing device 160 may be configuredto cluster the spatially static devices. The gateway device 140 and/orthe computing device 160 may be configured to cluster the spatiallystatic devices into different groups and/or areas of activity. Forexample, based on one or more users being in one area of activity, RSSIsassociated with spatially static devices in the area may experience asimilar disturbance. The gateway device 140 and/or the computing device160 may be configured to cluster the spatially static devices into areasbased on time of disturbances and/or magnitude of disturbancesexperienced during a period of time (e.g., a week, ten days, asexamples), such as a training period. For example, the first spatiallystatic device 112, the second spatially static device 114, and the thirdspatially static device 116 may experience similar disruptions. Thegateway device 140 and/or the computing device 160 may cluster the firstspatially static device 112, the second spatially static device 114, andthe third spatially static device 116. The fourth spatially staticdevice 122, the fifth spatially static device 124, and the sixthspatially static device 126 may experience similar disruptions. Thegateway device 140 and/or the computing device 160 may cluster thefourth spatially static device 122, the fifth spatially static device124, and the sixth spatially static device 126. The seventh spatiallystatic device 132, the eighth spatially static device 134, and the ninthspatially static device 136 may experience similar disruptions. Thegateway device 140 and/or the computing device 160 may cluster theseventh spatially static device 132, the eighth spatially static device134, and the ninth spatially static device 136.

The gateway device 140 and/or the computing device 160 may be configuredto determine patterns of user behavior at the premises 100. The patternsof user behavior may comprise a probability that there will be useractivity (e.g., user movement and/or presence) in an area 110, 120, 130of the premises 100 as a function of time and/or day. The gateway device140 and/or the computing device 160 may be configured to determine theprobability based on determining a duration of user activity within anarea 110, 120, 130 of the premises 100. The patterns of user behaviormay comprise patterns of transition of user activity between areas 110,120, 130 of the premises 100. The gateway device 140 and/or thecomputing device 160 may be configured to determine the patterns of userbehavior by determining time, duration, and/or transition of useractivity associated with areas 110, 120, 130 of the premises 100. Thegateway device 140 and/or the computing device 160 may be configured todetermine the time, duration, and/or transition of user activityassociated with areas 110, 120, 130 of the premises 100 for a period oftime (e.g., a week, ten days, etc.), such as a training period.

For example, a determined pattern of behavior may indicate that a userat the premises 100 may have a high probability (e.g., greater than orequal to 50%, 60%, 70%, 80%, 90%) of entering the first area 110 at oraround 5:00 p.m. on a weekday. The determined pattern of behavior mayindicate that five minutes after entering the first area 110, the usermay have a high probability of transitioning to the second area 120. Thedetermined pattern of behavior may indicate that thirty minutes afterentering the second area 120, the user may have a high probability oftransitioning to the third area 130.

The gateway device 140 and/or the computing device 160 may be configuredto determine abnormal user activity. The gateway device 140 and/or thecomputing device 160 may be configured to determine abnormal useractivity by determining that the probability of disruptions in RSSIs ofsignals from spatially static devices associated with an area 110, 120,130 at a time of day is low (e.g., equal than or less than 50%, 40%,30%, etc.). For example, disruptions in RSSI of signals from spatiallystatic devices associated with an area that are improbable may be causedby the presence of an intruder. Disruptions in RSSI of signals fromspatially static devices associated with an area that are improbable maybe caused by absence of a user, such as due to injury, illness, orincapacitation. Based on the determining abnormal user activity, thegateway device and/or the computing device 160 may be configured to sendan alert, such as to a device of another user, a healthcare provider, oran emergency responder.

FIG. 2 shows an example operating environment. The operating environmentmay comprise a premises (e.g., the premises 100 in FIG. 1). A gatewaydevice 240 (e.g., the gateway device 140 in FIG. 1) may be located atthe premises 200. One or more spatially static devices 220 (e.g., thespatially static devices 112-136 in FIG. 1) may be located at thepremises 200. The spatially static devices 220 may be configured tocommunicate with the gateway device 240. The gateway device 240 may beconfigured to receive signals from the devices located at the premises.The gateway device 240 may be configured to determine RSSI valuesassociated with the signals.

The RSSI values of signals received from the spatially static device 220may be affected by the presence of a user 280, such as in a direction ofpropagation of a signal from the spatially static device 220. The bodyof the user 280 may cause absorption and/or interference of signals fromthe spatially static device 220.

For example, the spatially static device 220 may send a signal 270. Theuser 280 may be present (e.g., standing, moving, etc.) through a path ofpropagation of the signal 270. The signal 270 may enter the user's 280body. At least a portion of the signal 270 may be absorbed by the user's280 body.

The signal 290, the signal 270 having passed through and/or emerged fromthe user's body 280, may be received by the gateway device 240. Thesignal 290 received from the gateway device 240 may have a differentRSSI than the signal 270 that the spatially static device 220 initiallytransmitted. For example, the RSSI of the signal 290 may be weaker thanthe RSSI of the signal 270.

The signal 290 may have a different RSSI than a signal received from thespatially static device 220 (e.g., by the gateway device 240) at adifferent time. For example, the signal 290 may have a different RSSIthan signals that are received from the spatially static device 220 whena user is not in the path of propagation, in an area proximate thespatially static device, and/or at the premises 200. The difference inRSSI of signals received from the spatially static device 220 atdifferent times may comprise a “fluctuation” in RSSI.

FIG. 3 shows example clusters of spatially static devices. The spatiallystatic devices 312, 314, 316, 322, 324, 326, 332, 334 (e.g., spatiallystatic devices 112, 114, 116, 122, 124, 126, 132, 134, 136 in FIG. 1)may be located at a premises (e.g., premises 100 in FIG. 1) Thespatially static devices may be located in a first area 310, a secondarea 320, or a third area 330 of a premises (e.g., the premises 100 inFIG. 1). The spatially static devices may be in communication with agateway device 240 (e.g., the gateway device 140 in FIG. 1). The gatewaydevice 340 may be located at the premises. The gateway device 340 may bein communication with a computing device located external to thepremises.

The gateway 340 and/or in the computing device may cluster the devicesinto areas and/or groups. The gateway 340 and/or in the computing devicemay cluster the devices into areas and/or groups based on RSSIs ofsignals output by the spatially static devices. The clusters of devicesmay comprise groups of devices that are spatially-related, such aswithin a particular room of area. RSSIs associated with signals outputby a cluster of spatially static devices may exhibit disruptions at thesame time or at similar times. Based on the RSSIs exhibiting similardisruptions, the gateway 340 and/or in the computing device may clusterthe group of spatially static devices in the area.

For example, as a result of one or more users being present in the firstarea 310 of the premises, the first spatially static device 312, thesecond spatially static device 314, and the third spatially staticdevice 316 may output signals having RSSIs with similar disruptions. Thespatially static devices 322, 324, 326, 332, 334, 336 that are not inthe first area 310 may output signals having RSSIs with dissimilar or nodisruptions. Based on the similarity of the disruptions in the RSSIs ofthe signals output by the spatially static devices in the first area310, the gateway device 340 and/or the computing device may cluster thefirst spatially static device 312, the second spatially static device314, and the third spatially static device 316 into a group. The gatewaydevice 340 and/or the computing device may determine that the group isassociated with the first area 310. The gateway device 340 and/or thecomputing device may perform similar steps to cluster the fourthspatially static device 322, the fifth spatially static device 324, andthe sixth spatially static device 326 in a group and/or determine thatthe group is associated with the second area 320. The gateway device 340and/or the computing device may perform similar steps to cluster theseventh spatially static device 332, the eighth spatially static device334, and the ninth spatially static device 336 in a group and/ordetermine that the group is associated with the third area 330.

FIG. 4 shows example pattern data. Graph 400 may show a probabilitydistribution associated with user activity. The graph 400 may beassociated with the first area 310 of the premises shown in FIG. 3. Ahorizontal axis of the graph 400 may represent a time of day. A verticalaxis (not shown) of the graph 400 may represent a probability associatedwith user activity in the first area 310. The graph 400 may show aprobability distribution associated with user activity in the first area310 at a particular time. A time 402 on the horizontal axis of the graph400 may correspond to a time when user activity in the first area 310 ismost likely. Data represented by the graph 400 may be generated,maintained, and/or analyzed by the gateway device 140 in FIG. 1 and/orthe gateway device 340 in FIG. 3 and/or an associated computing device.The data in graph 400 may be generated, maintained and analyzed by thecomputing device 160 in FIG. 1 and/or an associated computing device.

Graph 410 may show a probability distribution associated with useractivity. The graph 410 may be associated with the second area 320 ofthe premises shown in FIG. 3. A horizontal axis of the graph 410 mayrepresent a time of day. A vertical axis (not shown) of the graph 410may represent a probability associated with user activity in the secondarea 310. The graph 410 may show a probability distribution associatedwith user activity in the second area 320 at a particular time. A time412 on the horizontal axis of the graph 410 may correspond to a timewhen user activity in the second area 320 is most likely. Datarepresented by the graph 410 may be generated, maintained, and/oranalyzed by the gateway device 140 in FIG. 1, the gateway device gatewaydevice 440 in FIG. 4, and/or an associated computing device. The datarepresented by the graph 410 may be generated, maintained, and/oranalyzed by the computing device 160 in FIG. 1 and/or an associatedcomputing device.

Graph 420 may show a probability distribution associated with useractivity. The graph 420 may be associated with the third area 330 shownin FIG. 3. A horizontal axis of the graph 420 may represent a time ofday. A vertical axis (not shown) of the graph 420 may represent aprobability of a determined user activity in the third area 330. Thegraph 420 may show a probability distribution associated with useractivity in the third area 330 at a particular time. A time 422 on thehorizontal axis of the graph 420 may correspond to a time when useractivity in the third area 330 is most likely. Data represented by thegraph 420 may be generated, maintained, and/or analyzed by the gatewaydevice 340, the computing device, and/or an associated computing device.

FIG. 5 shows an example user activity pattern. Data represented by theprofile 500 of normal behavior may be generated, maintained, analyzed,and/or retrieved. The data represented by profile 500 may be generated,maintained, analyzed, and/or retrieved by the gateway device 140 in FIG.1, the gateway device 240 in FIG. 2, the gateway device 340 in FIG. 3,and/or an associated computing device. The data represented by theprofile 500 may be generated, maintained, analyzed, and/or retrieved bythe computing device 160 in FIG. 1 and/or an associated computingdevice. The profile 500 may indicate a transition probability acrossareas of activity 520. A graph associated with the profile 500 mayrepresent the transition probability across the areas of activity 520.FIG. 5 shows a representation of a first area 522, a second area 524,and a third area 526. The representation of the first area 522 maycorrespond to the first area 310 in FIG. 3. The representation of thesecond area 524 may correspond to the second area 320 in FIG. 3. Therepresentation of the third area 526 may correspond to the third area330 in FIG. 3. The transition probability may indicate a likelihood thata user would move across the areas 522, 524, 526. The transitionprobability may indicate an expected order of user activity associatedwith areas 522, 524, 526. The expected order of user activity maycomprise a sequence of spaces and/or areas that a user moves through.The expected order of user activity may comprise a speed of movement ofthe user. The expected order of user activity may comprise a time that auser spends in a space and/or area.

The profile 500 may comprise a probability of user activity with respectto time (e.g., time of day). The profile 500 may comprise and/or bebased on a determined user activity pattern and/or transitionprobability across the areas of activity 520. The profile 500 maycomprise a probability distribution of user activity (e.g., in the areasof activity 520) over time, as represented by the graph 510. Forexample, the graph 510 may show that activity (e.g., in an area ofactivity 520-522, 524, 526) is more likely to occur in the morning thanin the afternoon or the night. Based on a determination of user activityat a time that the profile 500 indicates that user activity is unlikely,abnormal user behavior may be determined. It may be determined that theprofile 500 indicates that user activity (e.g., in an area of activity520-522, 524, 526) is unlikely at the time based on the probabilitydistribution associated with normal behavior. For example, based ondetermined activity and night and the normal behavior pattern indicatingthat the probability of activity at night is less than 5%, abnormalbehavior may be determined. Based on user activity (e.g., in an area ofactivity 520-522, 524, 526) not being determined at a time when theprofile 500 indicates that user activity is likely, abnormal userbehavior may be determined. Based on user activity being different fromthe expected order of user activity and/or out of sequence of thetransition probability across areas of activity 520 of the profile 500,abnormal user behavior may be determined.

Based on determining abnormal user behavior, a notification of abnormaluser behavior may be generated. The notification may be generated by thegateway device 140 in FIG. 1 or the gateway device 340 in FIG. 3. Thenotification may be generated by the computing device 360 in FIG. 3. Thenotification may be sent to a device 530. The device 530 may beassociated with an emergency dispatch, a family member of a user, anemergency contact, a hospital, and/or healthcare personnel, as examples.

FIG. 6 shows an example method. At step 610, signals may be received.The signals may be received from a plurality of devices. The gatewaydevice 140 in FIG. 1 may receive the signals from the plurality ofdevices. The computing device 160 in FIG. 1 may receive signals from aplurality of devices. The signals may comprise Wi-Fi signals. Receivingthe signals may comprise receiving the signals within a period of time.

At step 620, one or more spatially static devices of the plurality ofdevices may be determined. Determining the one or more spatially staticdevices may comprise distinguishing spatially static devices fromnon-spatially static (e.g., mobile) devices of the plurality of devices.The one or more spatially static devices may be determined based onstrengths of the received signals. The one or more spatially staticdevices may be determined based on fluctuations in strengths of thereceived signals. For example, spatially static devices may exhibitfewer or smaller fluctuations in signal strength than non-spatiallystatic devices. The gateway device 140 in FIG. 1 may determine one ormore spatially static devices of the plurality of devices based onstrengths of the received signal. The gateway device 140 in FIG. 1 maydetermine one or more spatially static devices of the plurality ofdevices based on fluctuations in strengths of the received signals. Thecomputing device 160 in FIG. 1 may determine one or more spatiallystatic devices of the plurality of devices based on strengths of thereceived signals. The computing device 160 in FIG. 1 may determine oneor more spatially static devices of the plurality of devices based onfluctuations in strengths of the received signals. The received signalsmay have received signal strength indicators (RSSIs).

At step 630, a user activity pattern may be determined. The useractivity pattern may be determined based on changes in strengths ofsignals received from the spatially static devices. The activity patternmay be determined based on changes in strengths of signals received fromthe spatially static devices within a training time period. The gatewaydevice 140 in FIG. 1 may determine a user activity pattern based onchanges in strengths of signals received from the spatially staticdevices within a training time period. The computing device 160 in FIG.1 may determine a user activity pattern based on changes in strengths ofsignals received from the spatially static devices within a trainingtime period. The user activity pattern may be based on changes in thestrengths of the signals caused by absorption or interference of thesignals by one or more users. The user activity pattern may associateuser activity at a time of day with a probability.

At step 640, abnormal user activity may be determined. The abnormal useractivity may be determined based on the user activity pattern. Theabnormal user activity may be determined based on a strength of at leastone signal received from at least one of the spatially static devices.The abnormal user activity may be determined based on a strength of atleast one signal received from at least one of the spatially staticdevices after the training time period. The gateway device 140 in FIG. 1may determine abnormal user activity based on the user activity patternand a strength of at least one signal received from at least one of thespatially static devices. The computing device 160 in FIG. 1 maydetermine abnormal user activity based on the user activity pattern anda strength of at least one signal received from at least one of thespatially static devices. Determining abnormal user activity maycomprise determining that a change in the strength of at least onesignal deviates from a portion of a signal strength pattern associatedwith the training time period. The abnormal user activity may beassociated with at least one of abnormally low user activity orabnormally high user activity.

The determining the abnormal user activity may comprise determining theabnormal user activity in an area of a premises. Determining theabnormal user activity in the area of the premises may be based on RSSIsassociated with signals received from a group of devices located inanother area of the premises. The determining the abnormal user activitymay comprise determining a movement of a user between the area of thepremises and the other area of the premises. The determining theabnormal user activity may comprise determining that a probability ofthe user moving between the area of the premises and the other area ofthe premises may be equal to or less than a threshold probability. Thearea of the premises may be associated with a room of the premises. Theabnormal user activity may be associated with an intruder at thepremises.

An alert may be sent based on the abnormal user activity. The gatewaydevice 140 in FIG. 1 may send an alert based on the abnormal useractivity. The computing device 160 in FIG. 1 may send an alert based onthe abnormal user activity.

An alarm may be triggered based on the abnormal user activity. Thegateway device 140 in FIG. 1 may trigger an alarm based on the abnormaluser activity. The computing 160 in FIG. 1 may trigger an alarm based onthe abnormal user activity. An alarm may be caused to be triggered basedon the abnormal user activity. The gateway device 140 in FIG. 1 maycause an alarm to be triggered based on the abnormal user activity. Thecomputing 160 in FIG. 1 may cause an alarm to be triggered based on theabnormal user activity.

For example, a premises may comprise areas or rooms, such as a kitchenand a living room. A gateway device may be located at the premises. Asmart refrigerator and a smart dish washer may be located in thekitchen. A smart speaker and a smart television may be located in theliving room. A smart phone may be located at the premises. Therefrigerator, the dish washer, the smart speaker, the smart television,and the smart phone may be in communication with the gateway device viaWi-Fi. Wi-Fi signals received by the gateway device from the devices mayhave associated signal strength indicators (RSSIs). The gateway devicemay classify the refrigerator, the dish washer, the smart speaker, andthe smart television as spatially static devices based on a lowvariation in RSSI values of signals received by the gateway device overa training period. The gateway device may classify the smart phone as amobile device based on a high variation in RSSIs of signals received bythe gateway device.

Based on user activity occurring in the kitchen, signals having RSSIvalues lower than a baseline value may be received from therefrigerator. The signals may be received from a gateway device. Thebaseline value may be determined based on RSSI values when there is nouser activity in an area, such as the kitchen. For example, it may bedetermined that a baseline value may comprise an RSSI of −60. As anexample, the baseline value may be determined for the kitchen atmidnight, when there is no user activity. Signals having RSSI valueslower than a baseline value may be received from the IoT deviceassociated with the dish washer. RSSIs of signals received from thesmart speaker and the smart television may be equal to an expectedbaseline value. Based on the RSSIs and/or the expected baseline values,the refrigerator and the dish washer may be clustered into a first areaof activity.

Based on user activity occurring in the living room, the gateway devicemay receive signals having RSSIs lower than an expected baseline valuefrom the smart speaker. The gateway device may receive signals havingRSSIs lower than an expected baseline value from the smart television.RSSIs of signals received from the IoT device associated with therefrigerator and the IoT device associated with the dish washer mayremain equal to an expected baseline. The gateway device may cluster thesmart speaker and the smart television into a second area of activity.

The gateway device may generate a profile of normal user activity basedon RSSIs of signals received from the spatially static devices over atraining period. After generating the profile, the gateway device mayreceive an indication of user activity in the second area of activity at2 p.m. on a Monday. Based on the profile, the gateway device maydetermine that user activity in the second area of activity at 2 p.m. ona Monday is abnormal user activity. The gateway device may cause anotification indicative of the of abnormal user activity to be sent to adevice of a user associated with the premises.

FIG. 7 shows an example method. At step 710, one or more signals may bereceived. The signals may be received from one or more spatially staticdevices. The gateway device 140 in FIG. 1 may receive one or moresignals from one or more spatially static devices. The computing device160 in FIG. 1 may receive one or more signals from one or more spatiallystatic devices. The receiving of the one or more signals may comprisereceiving the one or more signals within a period of time. The one ormore signals may comprise Wi-Fi signals.

At step 720, a strength of the one or more signals may be determined.The gateway device 140 in FIG. 1 may determine a strength of the one ormore signals. The computing device 160 in FIG. 1 may determine astrength of the one or more signals. The strengths of the one or moresignals may comprise received signal strength indicators (RSSIs).

At step 730, a determination may be made that the strength of the one ormore signals deviates from a signal strength pattern. It may bedetermined that the strength deviates by at least a threshold value. Itmay be determined that the signal strength deviates from a signalstrength pattern associated with the one or more spatially staticdevices, such as by the threshold value. The gateway device 140 in FIG.1 may determine that the strength of the one or more signals deviates,by at least a threshold value, from a signal strength pattern associatedwith the one or more spatially static devices. The computing device 160in FIG. 1 may determine that the strength of the one or more signalsdeviates, by at least a threshold value, from a signal strength patternassociated with the one or more spatially static devices.

The determining that the strength of the one or more signals deviatesfrom the signal strength pattern by at least the threshold value maycomprise determining that a change in the strength of the one or moresignals over the period of time may deviate from a portion of the signalstrength pattern associated with the period of time. The signal strengthpattern may be based on changes in the strengths of the one or moresignals caused by absorption or interference of the signals by one ormore users. The signal strength pattern may associate a time of day witha probability. The probability may represent a probability ofexperiencing a disruption from an associated RSSI.

At step 740, abnormal user activity may be determined. The abnormal useractivity may be determined based on the strength deviating from thesignal strength pattern by at least the threshold value. The gatewaydevice 140 in FIG. 1 may determine abnormal user activity based on thestrength deviating from the signal strength pattern by at least thethreshold value. The computing device 160 in FIG. 1 may determineabnormal user activity based on the strength deviating from the signalstrength pattern by at least the threshold value. The abnormal useractivity may be associated with at least one of abnormally low useractivity or abnormally high user activity.

The determining the abnormal user activity may comprise determining theabnormal user activity in an area of a premises. Determining theabnormal user activity in the area of the premises may be based on RSSIsassociated with signals received from a group of devices. The group ofdevices may be located in another area of the premises. The determiningthe abnormal user activity may comprise determining a movement of a userbetween the area of the premises and the other area of the premises. Thedetermining the abnormal user activity may comprise determining that aprobability of the user moving between the area of the premises and theother area of the premises may be equal to or less than a thresholdprobability. The area of the premises may be associated with a room ofthe premises. The abnormal user activity may be caused by an intruder atthe premises.

The method may comprise sending an alert. The alert may be sent based onthe abnormal user activity. The gateway device 140 in FIG. 1 may send analert based on the abnormal user activity. The computing device 160 inFIG. 1 may send an alert based on the abnormal user activity. The methodmay comprise causing an alert to be sent based on the abnormal useractivity. The gateway device 140 in FIG. 1 may cause an alert to be sentbased on the abnormal user activity. The computing 160 in FIG. 1 maycause an alert to be sent based on the abnormal user activity.

The method may comprise triggering an alarm. The alarm may be triggeredbased on the abnormal user activity. The gateway device 140 in FIG. 1may trigger an alarm based on the abnormal user activity. The computingdevice 160 in FIG. 1 may trigger an alarm based on the abnormal useractivity. The method may comprise causing an alarm to be triggered basedon the abnormal user activity. The gateway device 140 in FIG. 1 maycause an alarm to be triggered based on the abnormal user activity. Thecomputing device 160 in FIG. 1 may cause an alarm to be triggered basedon the abnormal user activity.

A premises may comprise rooms and/or areas, such as a kitchen and aliving room. The premises may comprise a gateway device. The kitchen maycomprise an Internet of Things (IoT) device associated with arefrigerator and an IoT device associated with a dish washer. The livingroom may comprise a smart speaker and a smart television. A userassociated with the premises may comprise a smart phone.

The IoT device associated with the refrigerator, the IoT deviceassociated with the dish washer, the smart speaker, the smarttelevision, and the smart phone may be in communication with the gatewaydevice via Wi-Fi. The signals received by the gateway device from thedevices at the premises may have received signal strength indicators(RSSIs). The gateway device may classify the IoT device associated withthe refrigerator, the IoT device associated with the dish washer, thesmart speaker, and the smart television as spatially static devicesbased on a high percentage of connection with the gateway device and/ora low variation in RSSI values of signals received by the gateway deviceover a training period. The gateway device may classify the smart phoneas a mobile device based on a failure to maintain a high percentage ofconnection with the gateway device and/or a high variation in RSSIs ofsignals received by the gateway device.

Based on user activity occurring in the kitchen, the gateway device mayreceive signals having RSSI values lower than an expected baseline valuefrom the IoT device associated with the refrigerator. The gateway devicemay receive signals having RSSI values lower than an expected baselinevalue from the IoT device associated with the dish washer. The gatewaydevice may receive signals having RSSI values from the smart speaker andthe smart television equal to an expected baseline value.

Based on user activity occurring in the living room, the gateway devicemay receive RSSIs lower than expected baseline value from the smartspeaker and the smart television. The gateway device may receive signalsfrom the IoT device associated with the refrigerator and the IoT deviceassociated with the dish washer having RSSIs equal to an expectedbaseline. The gateway device may cluster the IoT device associated withthe refrigerator and the IoT device associated with the dish washer intoa first area of activity. The gateway device may cluster the smartspeaker and the smart television into a second area of activity.

The gateway device may generate a profile of normal user activity. Thegateway device may receive an indication of user activity in the secondarea of activity with no preceding user activity in the first area ofactivity. The profile may indicate that user activity in the second areaof activity with no preceding user activity in the first area ofactivity comprises abnormal user activity. The gateway device may causea notification of a possible intruder to be sent to an emergencydispatch system.

FIG. 8 shows an example method. At step 810, a group of devices of aplurality of spatially static devices may be determined. The group ofdevices may be located in an area of a premises. The group of devicesmay be determined based on one or more received signal strengthindicators (RSSI) associated with signals received from the plurality ofspatially static devices. The gateway device 140 in FIG. 1 may determinea group of devices of a plurality of spatially static devices that arelocated in an area of a premises based on one or more received signalstrength indicators (RSSI) associated with signals received from theplurality of spatially static devices located at the premises. Thecomputing device 160 in FIG. 1 may determine a group of devices of aplurality of spatially static devices that are located in an area of apremises based on one or more received signal strength indicators (RSSI)associated with signals received from the plurality of spatially staticdevices located at the premises. The area of the premises may beassociated with a room of the premises. The one or more RSSI may beassociated with one or more Wi-Fi signals.

At step 820, a pattern associated with user activity in the area of thepremises may be determined. The pattern may be determined based onchanges in one or more RSSI associated with signals received from thegroup of devices. The pattern may be determined based on changes in oneor more RSSI associated with signals received from the group of deviceswithin a first time period. The gateway device 140 in FIG. 1 maydetermine a pattern associated with user activity in the area of thepremises based on changes in one or more RSSI associated with signalsreceived from the group of devices within a first time period. Thecomputing device 160 in FIG. 1 may determine a pattern associated withuser activity in the area of the premises based on changes in one ormore RSSI associated with signals received from the group of deviceswithin a first time period. The first time period may comprise a periodfrom a first time of day to a second time of day. The pattern maycomprise a probability of an RSSI of a signal from at least one deviceof the group of devices at a time of day between the period from thefirst time of day to the second time of day. The pattern may be based onchanges in the one or more RSSIs caused by absorption or interference ofthe signals by one or more users.

At step 830, abnormal user activity in the area of the premises may bedetermined. The abnormal user activity may be determined based on thepattern. The abnormal user activity may be determined based on one ormore RSSI associated with signals received from the group of deviceswithin a second time period. The gateway device 140 in FIG. 1 maydetermine abnormal user activity in the area of the premises based onthe pattern and one or more RSSIs associated with signals received fromthe group of devices within a second time period. The computing device160 in FIG. 1 may determine abnormal user activity in the area of thepremises based on the pattern and one or more RSSIs associated withsignals received from the group of devices within a second time period.The first time period and the second time period may be associated witha time of day.

The determining the abnormal user activity in the area of the premisesmay be based on RSSI associated with signals received from another groupof devices. The other group of devices may be located in another area ofthe premises. The determining the abnormal user activity may comprisedetermining a movement of a user between the area of the premises andthe other area of the premises. The determining the abnormal useractivity may comprise determining that a probability of the user movingbetween the area of the premises and the other area of the premises maybe equal to or less than a threshold probability. The abnormal useractivity may be associated with an intruder at the premises. Theabnormal user activity may be associated with at least one of abnormallylow user activity or abnormally high user activity.

The method may comprise sending an alert. The alert may be sent based onthe abnormal user activity. The gateway device 140 in FIG. 1 may send analert based on the abnormal user activity. The computing device 160 inFIG. 1 may send an alert based on the abnormal user activity. The methodmay comprise causing an alert to be sent based on the abnormal useractivity. The gateway device 140 in FIG. 1 may cause an alert to be sentbased on the abnormal user activity. The computing device 160 in FIG. 1may cause an alert to be sent based on the abnormal user activity. Themethod may comprise triggering an alarm based on the abnormal useractivity. The gateway device 140 in FIG. 1 may trigger an alarm based onthe abnormal user activity. The computing device 160 in FIG. 1 maytrigger an alarm based on the abnormal user activity. The method maycomprise causing an alarm to be triggered based on the abnormal useractivity. The gateway device 140 in FIG. 1 may cause an alarm to betriggered based on the abnormal user activity. The computing device 160in FIG. 1 may cause an alarm to be triggered based on the abnormal useractivity.

A premises may comprise rooms and/or areas, such as a kitchen and aliving room. The premises may comprise a gateway device. The kitchen maycomprise an Internet of Things (IoT) device associated with arefrigerator and an IoT device associated with a dish washer. The livingroom may comprise a smart speaker and a smart television. A userassociated with the premises may comprise a smart phone. The IoT deviceassociated with the refrigerator, the IoT device associated with thedish washer, the smart speaker, the smart television, and the smartphone may be in communication with the gateway device via Wi-Fi signals.The Wi-Fi signals may have received signal strength indicators (RSSI).The gateway device may classify the IoT device associated with therefrigerator, the IoT device associated with the dish washer, the smartspeaker, and the smart television as spatially static devices based on ahigh percentage of connection with the gateway device and/or a lowvariation in RSSIs of signals received by the gateway device over atraining period. The gateway device may classify the smart phone as amobile device based on a failure to maintain a high percentage ofconnection with the gateway device and/or a high variation in RSSIs ofsignals received by the gateway device.

Based on user activity occurring in the kitchen, the gateway device mayreceive signals having RSSIs lower than an expected baseline value fromthe IoT device associated with the refrigerator. The gateway device mayreceive signals having RSSIs lower than an expected baseline value fromthe IoT device associated with the dish washer. The gateway device mayreceive signals having RSSIs from the smart speaker and the smarttelevision equal to an expected baseline.

Based on user activity occurring in the living room, the gateway devicemay receive signals having RSSIs lower than an expected baseline valuefrom the smart speaker. The gateway device may receive signals havingRSSIs lower than an expected baseline value from the smart television.The gateway device may receive signals from the IoT device associatedwith the refrigerator and the IoT device associated with the dish washerhaving RSSIs equal to an expected baseline. The gateway device maycluster the IoT device associated with the refrigerator and the IoTdevice associated with the dish washer into a first area of activity.The gateway device may cluster the smart speaker and the smarttelevision into a second area of activity.

The gateway device may generate a profile of normal user activity. Theprofile may indicate that user activity in the first area is probably at2:00 p.m. on a Saturday. The gateway device may not receive anindication of user activity in the first area at 2:00 p.m. on aSaturday. The gateway device may cause a notification indicative ofabnormal user activity to be sent to a device of a family member of auser associated with the premises.

FIG. 9 shows an example operating environment 900. The computingenvironment 900 is only an example of an operating environment and isnot intended to suggest any limitation as to the scope of use orfunctionality of operating environment architecture. Neither should theoperating environment be interpreted as having any dependency orrequirement relating to any one or combination of components shown inthe example operating environment.

The present methods and systems may be operational with numerous othergeneral purpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that may be suitable for use with the systems andmethods comprise, but are not limited to, personal computers, servercomputers, laptop devices, and multiprocessor systems. Additionalexamples comprise set top boxes, programmable consumer electronics,network PCs, minicomputers, mainframe computers, distributed computingenvironments that comprise any of the above systems or devices, and thelike.

The processing of the disclosed methods and systems may be performed bysoftware components. The disclosed systems and methods may be describedin the general context of computer-executable instructions, such asprogram modules, being executed by one or more computers or otherdevices. Generally, program modules comprise computer code, routines,programs, objects, components, data structures, that performs particulartasks or implement particular abstract data types. The disclosed methodsmay also be practiced in grid-based and distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices.

The spatially static devices 112, 114, 116, 122, 124, 126, 132, 134,136, gateway device 140, and computing device 160 in FIG. 1 may beimplemented in an instance of a computing device 900 in FIG. 9. Thesystems and methods disclosed herein may be implemented via ageneral-purpose computing device in the form of a computing device 901.The components of the computing device 901 may comprise, but are notlimited to, one or more processors or processing units 903, a systemmemory 912, and a system bus 913 that couples various system componentsincluding the processor 903 to the system memory 912. In the case ofmultiple processing units 903, the system may utilize parallelcomputing.

The system bus 913 represents one or more of several possible types ofbus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, and a processor or localbus using any of a variety of bus architectures. By way of example, sucharchitectures may comprise an Industry Standard Architecture (ISA) bus,a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, aVideo Electronics Standards Association (VESA) local bus, an AcceleratedGraphics Port (AGP) bus, and a Peripheral Component Interconnects (PCI),a PCI-Express bus, a Personal Computer Memory Card Industry Association(PCMCIA), Universal Serial Bus (USB) and the like. The bus 913, and allbuses specified in this description may also be implemented over a wiredor wireless network connection and each of the subsystems, including theprocessor 903, a mass storage device 904, an operating system 905,premises monitoring software 906, premises monitoring data 907, anetwork adapter 908, system memory 912, an Input/Output Interface 910, adisplay adapter 909, a display device 911, and a human machine interface902, may be contained within one or more remote computing devices 914a,b,c at physically separate locations, connected through buses of thisform, in effect implementing a fully distributed system.

The computing device 901 typically comprises a variety of computerreadable media. Example readable media may be any available media thatis accessible by the computing device 901 and comprises, for example andnot meant to be limiting, both volatile and non-volatile media,removable and non-removable media. The system memory 912 comprisescomputer readable media in the form of volatile memory, such as randomaccess memory (RAM), and/or non-volatile memory, such as read onlymemory (ROM). The system memory 912 typically contains data such aspremises monitoring data 907 and/or program modules such as operatingsystem 905 and premises monitoring software 906 that are immediatelyaccessible to and/or are presently operated on by the processing unit903.

The computing device 901 may comprise other removable/non-removable,volatile/non-volatile computer storage media. By way of example, FIG. 9shows a mass storage device 904 which may provide non-volatile storageof computer code, computer readable instructions, data structures,program modules, and other data for the computing device 901. Forexample and not meant to be limiting, a mass storage device 904 may be ahard disk, a removable magnetic disk, a removable optical disk, magneticcassettes or other magnetic storage devices, flash memory cards, CD-ROM,digital versatile disks (DVD) or other optical storage, random accessmemories (RAM), read only memories (ROM), electrically erasableprogrammable read-only memory (EEPROM), and the like.

Optionally, any number of program modules may be stored on the massstorage device 904, including by way of example, an operating system 905and premises monitoring software 906. Each of the operating system 905and premises monitoring software 906 (or some combination thereof) maycomprise elements of the programming and the premises monitoringsoftware 906. Premises monitoring data 907 may also be stored on themass storage device 904. Premises monitoring data 907 may be stored inany of one or more databases known in the art. Examples of suchdatabases comprise, DB2®, Microsoft® Access, Microsoft® SQL Server,Oracle®, mySQL, PostgreSQL, and the like. The databases may becentralized or distributed across multiple systems.

The user may enter commands and information into the computing device901 via an input device (not shown). Examples of such input devicescomprise, but are not limited to, a keyboard, pointing device (e.g., a“mouse”), a microphone, a joystick, a scanner, tactile input devicessuch as gloves, and other body coverings, and the like These and otherinput devices may be connected to the processing unit 803 via a humanmachine interface 902 that is coupled to the system bus 913, but may beconnected by other interface and bus structures, such as a parallelport, game port, an IEEE 1394 Port (also known as a Firewire port), aserial port, or a universal serial bus (USB).

A display device 911 may also be connected to the system bus 913 via aninterface, such as a display adapter 909. It is contemplated that thecomputing device 901 may have more than one display adapter 909 and thecomputing device 901 may have more than one display device 911. Forexample, a display device may comprise a monitor, an LCD (Liquid CrystalDisplay), or a projector. In addition to the display device 911, otheroutput peripheral devices may comprise components such as speakers (notshown) and a printer (not shown) which may be connected to the computingdevice 901 via Input/Output Interface 910. Any step and/or result of themethods may be output in any form to an output device. Such output maycomprise any form of visual representation, including, but not limitedto, textual, graphical, animation, audio, tactile, and the like. Thedisplay 911 and computing device 901 may be part of one device, orseparate devices.

The computing device 901 may operate in a networked environment usinglogical connections to one or more remote computing devices 914 a,b,c.By way of example, a remote computing device may comprise a personalcomputer, portable computer, a smart phone, a server, a router, anetwork computer, a peer device or other common network node, and so on.Logical connections between the computing device 901 and a remotecomputing device 914 a,b,c may be made via a network 915, such as alocal area network (LAN) and a general wide area network (WAN). Suchnetwork connections may be through a network adapter 908. A networkadapter 908 may be implemented in both wired and wireless environments.Such networking environments are conventional and commonplace indwellings, offices, enterprise-wide computer networks, intranets, andthe Internet.

Application programs and other executable program components such as theoperating system 905 are shown herein as discrete blocks, although it isrecognized that such programs and components reside at various times indifferent storage components of the computing device 901, and areexecuted by the data processor(s) of the computer. An implementation ofpremises monitoring software 906 may be stored on or sent across someform of computer readable media. Any of the disclosed methods may beperformed by computer readable instructions embodied on computerreadable media. Computer readable media may comprise any available mediathat may be accessed by a computer. By way of example and not meant tobe limiting, computer readable media may comprise “computer storagemedia” and “communications media.” “Computer storage media” comprisevolatile and non-volatile, removable and non-removable media implementedin any methods or technology for storage of information such as computerreadable instructions, data structures, program modules, or other data.Example computer storage media comprises, but is not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which may be used to store the desired informationand which may be accessed by a computer.

What is claimed:
 1. A method comprising: receiving, from a plurality ofdevices, wireless communication signals; determining, based on strengthsof the received wireless communication signals, one or more spatiallystatic devices of the plurality of devices; determining, based onchanges in strengths of wireless communication signals received from theone or more spatially static devices within a time period, a useractivity pattern; and determining, based on the user activity patternand a strength of at least one wireless communication signal receivedfrom at least one of the spatially static devices after the time period,abnormal user activity.
 2. The method of claim 1, wherein the strengthof a received wireless communication signal is indicated by a receivedsignal strength indicator (RSSI) associated with the received wirelesscommunication signal.
 3. The method of claim 1, wherein the determiningthe one or more spatially static devices is based on a fluctuation ofthe strengths of the received wireless communication signals.
 4. Themethod of claim 1, wherein the user activity pattern is based on changesin the strengths of the wireless communication signals caused byabsorption or interference of the wireless communication signals by oneor more users.
 5. The method of claim 1, wherein the method furthercomprises sending an alert based on the abnormal user activity.
 6. Themethod of claim 1, wherein the method further comprises triggering analarm based on the abnormal user activity.
 7. The method of claim 1,wherein the one or more spatially static devices comprise one or more ofa wearable device, a camera, a lighting device, a thermostat, a motionsensor, a gateway device, or a virtual assistant.
 8. A methodcomprising: determining, based on one or more received signal strengthindicators (RSSI) associated with signals received from a plurality ofspatially static devices located at a premises, a group of devices ofthe plurality of spatially static devices that are located in an area ofthe premises; determining, based on changes in one or more RSSIassociated with signals received from the group of devices within afirst time period, a pattern associated with user activity in the areaof the premises; and determining, based on the pattern and one or moreRSSI associated with signals received from the group of devices within asecond time period, abnormal user activity in the area of the premises.9. The method of claim 8, wherein the first time period comprises aperiod from a first time of day to a second time of day; and wherein thepattern comprises a probability of an RSSI of a signal from at least onedevice of the group of devices at a time of day between the period fromthe first time of day to the second time of day.
 10. The method of claim8, wherein the determining the abnormal user activity in the area of thepremises is further based on RSSI associated with signals received fromanother group of devices located in another area of the premises. 11.The method of claim 10, wherein the determining the abnormal useractivity comprises determining a movement of a user between the area ofthe premises and the another area of the premises.
 12. The method ofclaim 11, wherein the determining the abnormal user activity comprisesdetermining that a probability of the user moving between the area ofthe premises and the another area of the premises is equal to or lessthan a threshold probability.
 13. The method of claim 8, wherein thearea of the premises is associated with a room of the premises.
 14. Themethod of claim 8, wherein the abnormal user activity is associated withan intruder at the premises.
 15. A computing device comprising: one ormore processors; and memory storing instructions that, when executed bythe one or more processors, cause the computing device to: receive, froma plurality of devices, wireless communication signals; determine, basedon strengths of the received wireless communication signals, one or morespatially static devices of the plurality of devices; determine, basedon changes in strengths of wireless communication signals received fromthe one or more spatially static devices within a time period, a useractivity pattern; and determine, based on the user activity pattern anda strength of at least one wireless communication signal received fromat least one of the spatially static devices after the time period,abnormal user activity.
 16. The computing device of claim 15, whereinthe instructions, when executed, further cause the computing device todetermine the strength of a received wireless communication signal basedat least in part on a received signal strength indicator (RSSI)associated with the received wireless communication signal.
 17. Thecomputing device of claim 15, wherein the instructions, when executed,further cause the computing device to determine the one or morespatially static devices based at least in part on a fluctuation of thestrengths of the received wireless communication signals.
 18. Thecomputing device of claim 15, wherein the instructions, when executed,further cause the computing device to determine the user activitypattern based at least in part on changes in the strengths of thewireless communication signals caused by absorption or interference ofthe wireless communication signals by one or more users.
 19. Thecomputing device of claim 15, wherein the instructions, when executed,further cause the computing device to send an alert based at least inpart on the abnormal user activity.
 20. The computing device of claim15, wherein the instructions, when executed, further cause the computingdevice to trigger an alarm based on the abnormal user activity.
 21. Thecomputing device of claim 15, wherein the one or more spatially staticdevices comprise one or more of a wearable device, a camera, a lightingdevice, a thermostat, a motion sensor, a gateway device, or a virtualassistant.
 22. A system comprising: a plurality of devices; and acomputing device configured to: receive, from the plurality of devices,wireless communication signals; determine, based on strengths of thereceived wireless communication signals, one or more spatially staticdevices of the plurality of devices; determine, based on changes instrengths of wireless communication signals received from the one ormore spatially static devices within a time period, a user activitypattern; and determine, based on the user activity pattern and astrength of at least one wireless communication signal received from atleast one of the spatially static devices after the time period,abnormal user activity.
 23. The system of claim 22, wherein the strengthof a received wireless communication signal is indicated by a receivedsignal strength indicator (RSSI) associated with the received wirelesscommunication signal.
 24. The system of claim 22, wherein the computingdevice determines the one or more spatially static devices based atleast in part on a fluctuation of the strengths of the received wirelesscommunication signals.
 25. The system of claim 22, wherein the computingdevice determines the user activity pattern based at least in part onchanges in the strengths of the wireless communication signals caused byabsorption or interference of the wireless communication signals by oneor more users.
 26. The system of claim 22, wherein the computing deviceis further configured to send an alert based on the abnormal useractivity.
 27. The system of claim 22, wherein the computing device isfurther configured to trigger an alarm based on the abnormal useractivity.
 28. The system of claim 22, wherein the one or more spatiallystatic devices comprise one or more of a wearable device, a camera, alighting device, a thermostat, a motion sensor, a gateway device, or avirtual assistant.